Instructions to use ProbeX/Model-J__ResNet__model_idx_0816 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0816 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0816") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0816") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0816", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9df3d1f86ee6432da4400d6bdcde247a74de878a0164956a5c29a46b618bec2f
- Size of remote file:
- 5.37 kB
- SHA256:
- c91affe10319adfb16bde47e884337c425888d6916e86cf5152fde60db3d9b66
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